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Plachta
2023-02-26 20:08:37 +08:00
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finetune_speaker.py
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import os
import json
import argparse
import itertools
import math
import torch
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.cuda.amp import autocast, GradScaler
from tqdm import tqdm
import librosa
import logging
logging.getLogger('numba').setLevel(logging.WARNING)
import commons
import utils
from data_utils import (
TextAudioSpeakerLoader,
TextAudioSpeakerCollate,
DistributedBucketSampler
)
from models import (
SynthesizerTrn,
MultiPeriodDiscriminator,
)
from losses import (
generator_loss,
discriminator_loss,
feature_loss,
kl_loss
)
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
torch.backends.cudnn.benchmark = True
global_step = 0
def main():
"""Assume Single Node Multi GPUs Training Only"""
assert torch.cuda.is_available(), "CPU training is not allowed."
n_gpus = torch.cuda.device_count()
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '8000'
hps = utils.get_hparams()
mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
def run(rank, n_gpus, hps):
global global_step
symbols = hps['symbols']
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(rank)
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size,
[32,300,400,500,600,700,800,900,1000],
num_replicas=n_gpus,
rank=rank,
shuffle=True)
collate_fn = TextAudioSpeakerCollate()
train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False, pin_memory=True,
collate_fn=collate_fn, batch_sampler=train_sampler)
# train_loader = DataLoader(train_dataset, batch_size=hps.train.batch_size, num_workers=0, shuffle=False, pin_memory=True,
# collate_fn=collate_fn)
if rank == 0:
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(eval_dataset, num_workers=0, shuffle=False,
batch_size=hps.train.batch_size, pin_memory=True,
drop_last=False, collate_fn=collate_fn)
net_g = SynthesizerTrn(
len(symbols),
hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
n_speakers=hps.data.n_speakers,
**hps.model).cuda(rank)
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
# load existing model
_, _, _, _ = utils.load_checkpoint("./pretrained_models/G_trilingual.pth", net_g, None)
_, _, _, _ = utils.load_checkpoint("./pretrained_models/D_trilingual.pth", net_d, None)
epoch_str = 1
global_step = 0
# freeze all other layers except speaker embedding
for p in net_g.parameters():
p.requires_grad = True
for p in net_d.parameters():
p.requires_grad = True
# for p in net_d.parameters():
# p.requires_grad = False
# net_g.emb_g.weight.requires_grad = True
optim_g = torch.optim.AdamW(
net_g.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps)
optim_d = torch.optim.AdamW(
net_d.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps)
# optim_d = None
net_g = DDP(net_g, device_ids=[rank])
net_d = DDP(net_d, device_ids=[rank])
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay)
scaler = GradScaler(enabled=hps.train.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank==0:
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, eval_loader], logger, [writer, writer_eval])
else:
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler, [train_loader, None], None, None)
scheduler_g.step()
scheduler_d.step()
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
net_g, net_d = nets
optim_g, optim_d = optims
scheduler_g, scheduler_d = schedulers
train_loader, eval_loader = loaders
if writers is not None:
writer, writer_eval = writers
# train_loader.batch_sampler.set_epoch(epoch)
global global_step
net_g.train()
net_d.train()
for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(tqdm(train_loader)):
x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)
spec, spec_lengths = spec.cuda(rank, non_blocking=True), spec_lengths.cuda(rank, non_blocking=True)
y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)
speakers = speakers.cuda(rank, non_blocking=True)
with autocast(enabled=hps.train.fp16_run):
y_hat, l_length, attn, ids_slice, x_mask, z_mask,\
(z, z_p, m_p, logs_p, m_q, logs_q) = net_g(x, x_lengths, spec, spec_lengths, speakers)
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax)
y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
y_hat_mel = mel_spectrogram_torch(
y_hat.squeeze(1),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax
)
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
# Discriminator
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
with autocast(enabled=False):
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
loss_disc_all = loss_disc
optim_d.zero_grad()
scaler.scale(loss_disc_all).backward()
scaler.unscale_(optim_d)
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
scaler.step(optim_d)
with autocast(enabled=hps.train.fp16_run):
# Generator
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
with autocast(enabled=False):
loss_dur = torch.sum(l_length.float())
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
loss_fm = feature_loss(fmap_r, fmap_g)
loss_gen, losses_gen = generator_loss(y_d_hat_g)
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_dur + loss_kl
optim_g.zero_grad()
scaler.scale(loss_gen_all).backward()
scaler.unscale_(optim_g)
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
scaler.step(optim_g)
scaler.update()
if rank==0:
if global_step % hps.train.log_interval == 0:
lr = optim_g.param_groups[0]['lr']
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_dur, loss_kl]
logger.info('Train Epoch: {} [{:.0f}%]'.format(
epoch,
100. * batch_idx / len(train_loader)))
logger.info([x.item() for x in losses] + [global_step, lr])
scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr, "grad_norm_g": grad_norm_g}
scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/dur": loss_dur, "loss/g/kl": loss_kl})
scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
image_dict = {
"slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
"slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
"all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
"all/attn": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy())
}
utils.summarize(
writer=writer,
global_step=global_step,
images=image_dict,
scalars=scalar_dict)
if global_step % hps.train.eval_interval == 0:
evaluate(hps, net_g, eval_loader, writer_eval)
utils.save_checkpoint(net_g, None, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
utils.save_checkpoint(net_g, None, hps.train.learning_rate, epoch,
os.path.join(hps.model_dir, "G_latest.pth".format(global_step)))
# utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
old_g=os.path.join(hps.model_dir, "G_{}.pth".format(global_step-4000))
# old_d=os.path.join(hps.model_dir, "D_{}.pth".format(global_step-400))
if os.path.exists(old_g):
os.remove(old_g)
# if os.path.exists(old_d):
# os.remove(old_d)
global_step += 1
if epoch > hps.max_epochs:
print("Maximum epoch reached, closing training...")
exit()
if rank == 0:
logger.info('====> Epoch: {}'.format(epoch))
def evaluate(hps, generator, eval_loader, writer_eval):
generator.eval()
with torch.no_grad():
for batch_idx, (x, x_lengths, spec, spec_lengths, y, y_lengths, speakers) in enumerate(eval_loader):
x, x_lengths = x.cuda(0), x_lengths.cuda(0)
spec, spec_lengths = spec.cuda(0), spec_lengths.cuda(0)
y, y_lengths = y.cuda(0), y_lengths.cuda(0)
speakers = speakers.cuda(0)
# remove else
x = x[:1]
x_lengths = x_lengths[:1]
spec = spec[:1]
spec_lengths = spec_lengths[:1]
y = y[:1]
y_lengths = y_lengths[:1]
speakers = speakers[:1]
break
y_hat, attn, mask, *_ = generator.module.infer(x, x_lengths, speakers, max_len=1000)
y_hat_lengths = mask.sum([1,2]).long() * hps.data.hop_length
mel = spec_to_mel_torch(
spec,
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.mel_fmin,
hps.data.mel_fmax)
y_hat_mel = mel_spectrogram_torch(
y_hat.squeeze(1).float(),
hps.data.filter_length,
hps.data.n_mel_channels,
hps.data.sampling_rate,
hps.data.hop_length,
hps.data.win_length,
hps.data.mel_fmin,
hps.data.mel_fmax
)
image_dict = {
"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy())
}
audio_dict = {
"gen/audio": y_hat[0,:,:y_hat_lengths[0]]
}
if global_step == 0:
image_dict.update({"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())})
audio_dict.update({"gt/audio": y[0,:,:y_lengths[0]]})
utils.summarize(
writer=writer_eval,
global_step=global_step,
images=image_dict,
audios=audio_dict,
audio_sampling_rate=hps.data.sampling_rate
)
generator.train()
if __name__ == "__main__":
main()
preprocess.py
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import os
if __name__ == "__main__":
# load sampled_audio4ft
with open("sampled_audio4ft.txt", 'r', encoding='utf-8') as f:
old_annos = f.readlines()
num_old_voices = len(old_annos)
# load user text
with open("./user_voice/user_voice.txt.cleaned", 'r', encoding='utf-8') as f:
user_annos = f.readlines()
# check how many voices are recorded
wavfiles = [file for file in list(os.walk("./user_voice"))[0][2] if file.endswith(".wav")]
num_user_voices = len(wavfiles)
# user voices need to occupy 1/4 of the total dataset
if num_user_voices:
user_duplicate = num_old_voices // num_user_voices // 3
else:
user_duplicate = 0
# find corresponding existing annotation lines
actual_user_annos = ["./user_voice/" + line for line in user_annos if line.split("|")[0] in wavfiles]
final_annos = old_annos + actual_user_annos * user_duplicate
# load custom characters
if os.path.exists("custom_character_anno.txt"):
with open("custom_character_anno.txt", 'r', encoding='utf-8') as f:
custom_character_anno = f.readlines()
if len(custom_character_anno):
# custom character voices need to be at least equal to number of sample_audio4ft
num_character_voices = len(custom_character_anno)
cc_duplicate = num_old_voices // num_character_voices
if cc_duplicate == 0:
cc_duplicate = 1
final_annos = final_annos + custom_character_anno * cc_duplicate
# save annotation file
with open("final_annotation_train.txt", 'w', encoding='utf-8') as f:
for line in final_annos:
f.write(line)
# save annotation file for validation
with open("final_annotation_val.txt", 'w', encoding='utf-8') as f:
for line in actual_user_annos:
f.write(line)
if os.path.exists("custom_character_anno.txt"):
for line in custom_character_anno:
f.write(line)
user_voice/user_voice.txt
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0.wav|999|[ZH]所以，人的内在拥有对于人的幸福才是最关键的。[ZH]
1.wav|999|[ZH]正因为在大多数情形下人的自身内在相当贫乏，[ZH]
2.wav|999|[ZH]所以，那些再也用不着与生活的匮乏作斗争的人，[ZH]
3.wav|999|[ZH]他们之中的大多数从根本上还是感觉闷闷不乐。[ZH]
4.wav|999|[ZH]情形就跟那些还在生活的困苦中搏斗的人一般无异。[ZH]
5.wav|999|[ZH]他们内在空虚、感觉意识呆滞、思想匮乏，[ZH]
6.wav|999|[ZH]这些就驱使他们投入社交人群中。[ZH]
7.wav|999|[ZH]组成那些社交圈子的人也正是他们这一类的人。[ZH]
8.wav|999|[ZH]“因为相同羽毛的鸟聚在一块”。[ZH]
9.wav|999|[ZH]他们聚在一块追逐消遣、娱乐。[ZH]
10.wav|999|[ZH]他们以放纵感官的欢娱、极尽声色的享受开始，[ZH]
11.wav|999|[ZH]以荒唐、无度而告终。[ZH]
12.wav|999|[ZH]众多刚刚踏入生活的纨绔子弟穷奢极欲，[ZH]
13.wav|999|[ZH]在令人难以置信的极短时间内就把大部分家财挥霍殆尽。[ZH]
14.wav|999|[ZH]这种做派，其根源确实不是别的，正是无聊[ZH]
18.wav|999|[ZH]它源自上述的精神贫乏和空虚。[ZH]
16.wav|999|[ZH]一个外在富有、但内在贫乏的富家子弟来到这个世界，[ZH]
17.wav|999|[ZH]会徒劳地用外在的财富去补偿内在的不足；[ZH]
18.wav|999|[ZH]他渴望从外部得到一切，[ZH]
19.wav|999|[ZH]这情形就好比试图以少女的汗水去强健自己体魄的老朽之人。[ZH]
20.wav|999|[ZH]人自身内在的贫乏由此导致了外在财富的贫乏。[ZH]
21.wav|999|[ZH]至于另外两项人生好处的重要性，[ZH]
22.wav|999|[ZH]不需要我特别强调。[ZH]
23.wav|999|[ZH]财产的价值在当今是人所公认的，[ZH]
24.wav|999|[ZH]用不着为其宣传介绍。[ZH]
25.wav|999|[ZH]比起第二项的好处，[ZH]
26.wav|999|[ZH]第三项的好处具有一种相当飘渺的成分，[ZH]
27.wav|999|[ZH]因为名誉、名望、地位等[ZH]
28.wav|999|[ZH]全由他人的意见构成。[ZH]
29.wav|999|[ZH]每人都可以争取得到名誉，[ZH]
30.wav|999|[ZH]亦即清白的名声；[ZH]
31.wav|999|[ZH]但社会地位，则只有月盼国家政府的人才能染指；[ZH]
32.wav|999|[ZH]至于显赫的名望就只有极少数人才会得到。[ZH]
33.wav|999|[ZH]在所有这些当中，[ZH]
34.wav|999|[ZH]名誉是弥足珍贵的；[ZH]
35.wav|999|[ZH]显赫的名望则是人所希望得到的价值至昂的东西，[ZH]
36.wav|999|[ZH]那是天之骄子才能得到的金羊毛。[ZH]
37.wav|999|[ZH]另一方面，[ZH]
38.wav|999|[ZH]只有傻瓜才会把社会地位放置在财产之前。[ZH]
39.wav|999|[ZH]另外，人拥有的财产、物品和名誉、声望，[ZH]
40.wav|999|[ZH]是处于一种所谓的互为影响、促进的关系。[ZH]
41.wav|999|[ZH]彼得尼斯说过：“一个人所拥有的财产决定了这个人在他人眼中的价值”。[ZH]
42.wav|999|[ZH]如果这句话是正确的话，[ZH]
43.wav|999|[ZH]那么，反过来，他人对自己的良好评价，[ZH]
44.wav|999|[ZH]能以各种形式帮助自己获取财产。[ZH]
45.wav|999|[ZH]能以各种形式帮助自己获取财产。[ZH]
46.wav|999|[EN]So far, the problems have occurred in the US.[EN]
47.wav|999|[EN]The statement contained no surprises.[EN]
48.wav|999|[EN]The performance continues to improve.[EN]
49.wav|999|[EN]That would be a danger.[EN]
50.wav|999|[EN]Frankly, we were lucky to get second.[EN]
51.wav|999|[EN]However, no further action was taken by police.[EN]
52.wav|999|[EN]It depends on Labour, not on us.[EN]
53.wav|999|[EN]I had to be into it.[EN]
54.wav|999|[EN]My view has always been the same.[EN]
55.wav|999|[EN]In fact, he is not even in the squad for the game.[EN]
56.wav|999|[EN]It is set in Paris.[EN]
57.wav|999|[EN]Do you think we're a top nation ?[EN]
58.wav|999|[EN]Is our children learning ?[EN]
59.wav|999|[EN]How independent is that ?[EN]
60.wav|999|[EN]What form did that take ?[EN]
61.wav|999|[EN]Are they free ?[EN]
62.wav|999|[EN]The party has never fully recovered.[EN]
63.wav|999|[EN]In many ways, that is as important.[EN]
64.wav|999|[EN]His leader wanted to celebrate.[EN]
65.wav|999|[EN]It is not an option, but a policy requirement.[EN]
66.wav|999|[EN]Of further privacy, he had no need.[EN]
67.wav|999|[EN]He was good, but not that good.[EN]
68.wav|999|[EN]The results are expected within days.[EN]
69.wav|999|[EN]The company still had the confidence of its bankers, he said.[EN]
70.wav|999|[EN]Mr Crawford is no stranger to Scottish Enterprise.[EN]
71.wav|999|[EN]We were well worth the win.[EN]
72.wav|999|[EN]And it was this one.[EN]
73.wav|999|[EN]But that's another story for another day.[EN]
74.wav|999|[EN]But it was also hard.[EN]
75.wav|999|[EN]If so, it is a funny time to introduce it.[EN]
76.wav|999|[EN]We're looking for unity in the council.[EN]
77.wav|999|[EN]By then, a massive legal battle is likely to have started.[EN]
78.wav|999|[EN]They are not going the length of the pitch.[EN]
79.wav|999|[EN]However, he lasted only five days before going on the run.[EN]
80.wav|999|[EN]The concerns are the same.[EN]
81.wav|999|[EN]That was a bonus, but it was not the main objective.[EN]
82.wav|999|[EN]Scotland won by six wickets.[EN]
83.wav|999|[EN]It has to be enforced.[EN]
84.wav|999|[EN]And we can do it.[EN]
85.wav|999|[EN]Every one is a winner.[EN]
86.wav|999|[EN]Drugs are used a lot at the fishing, not just cannabis.[EN]
87.wav|999|[EN]They have set the standard.[EN]
88.wav|999|[EN]This is a major property in Edinburgh.[EN]
89.wav|999|[EN]It is a crisis of human rights, a crisis of leadership.[EN]
90.wav|999|[EN]The existing structure is in trouble.[EN]
91.wav|999|[EN]It all started with a visiting rugby club.[EN]
92.wav|999|[EN]He refused to reveal the reasons for the split.[EN]
user_voice/user_voice.txt.cleaned
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0.wav|999|swo↓↑i↓↑, ɹ`ən↑ t⁼ə neɪ↓ts⁼aɪ↓ jʊŋ→joʊ↓↑ t⁼weɪ↓ɥ↑ ɹ`ən↑ t⁼ə ʃiŋ↓fu↑ tsʰaɪ↑ s`ɹ`↓ ts⁼weɪ↓ k⁼wan→tʃ⁼jɛn↓ t⁼ə.
1.wav|999|ts`⁼əŋ↓ in→weɪ↓ ts⁼aɪ↓ t⁼a↓t⁼wo→s`u↓ tʃʰiŋ↑ʃiŋ↑ ʃja↓ɹ`ən↑ t⁼ə ts⁼ɹ↓s`ən→ neɪ↓ts⁼aɪ↓ ʃiɑŋ→t⁼ɑŋ→ pʰin↑fa↑,
2.wav|999|swo↓↑i↓↑, na↓ʃiɛ→ ts⁼aɪ↓iɛ↓↑ jʊŋ↓p⁼u↓ts`⁼ə ɥ↓↑ s`əŋ→xwo↑ t⁼ə kʰweɪ↓fa↑ ts⁼wo↓ t⁼oʊ↓ts`⁼əŋ→ t⁼ə ɹ`ən↑,
3.wav|999|tʰa→mən ts`⁼ɹ`→ts`⁼ʊŋ→ t⁼ə t⁼a↓t⁼wo→s`u↓ tsʰʊŋ↑k⁼ən→p⁼ən↓↑s`ɑŋ↓ xaɪ↑s`ɹ`↓ k⁼an↓↑tʃ⁼ɥɛ↑ mən↓mən↓p⁼u↓lə↓.
4.wav|999|tʃʰiŋ↑ʃiŋ↑ tʃ⁼joʊ↓ k⁼ən→ na↓ʃiɛ→ xaɪ↑ ts⁼aɪ↓ s`əŋ→xwo↑ t⁼ə kʰwən↓kʰu↓↑ ts`⁼ʊŋ→ p⁼wo↑t⁼oʊ↓ t⁼ə ɹ`ən↑ i↓p⁼an→ u↑i↓.
5.wav|999|tʰa→mən neɪ↓ts⁼aɪ↓ kʰʊŋ→ʃɥ→, k⁼an↓↑tʃ⁼ɥɛ↑ i↓s`ɹ`↑ t⁼aɪ→ts`⁼ɹ`↓, sɹ→ʃiɑŋ↓↑ kʰweɪ↓fa↑,
6.wav|999|ts`⁼ə↓ʃiɛ→ tʃ⁼joʊ↓ tʃʰɥ→s`ɹ`↓↑ tʰa→mən tʰoʊ↑ɹ`u↓ s`ə↓tʃ⁼iɑʊ→ ɹ`ən↑tʃʰɥn↑ ts`⁼ʊŋ→.
7.wav|999|ts⁼u↓↑ts`ʰəŋ↑ na↓ʃiɛ→ s`ə↓tʃ⁼iɑʊ→tʃʰɥæn→ts⁼ɹ t⁼ə ɹ`ən↑ iɛ↓↑ ts`⁼əŋ↓s`ɹ`↓ tʰa→mən ts`⁼ə↓ i→leɪ↓ t⁼ə ɹ`ən↑.
8.wav|999|“ in→weɪ↓ ʃiɑŋ→tʰʊŋ↑ ɥ↓↑mɑʊ↑ t⁼ə niɑʊ↓↑ tʃ⁼ɥ↓ ts⁼aɪ↓ i→kʰwaɪ↓”.
9.wav|999|tʰa→mən tʃ⁼ɥ↓ts⁼aɪ↓ i→kʰwaɪ↓ ts`⁼weɪ→ts`⁼u↑ ʃiɑʊ→tʃʰjɛn↓↑, ɥ↑lə↓.
10.wav|999|tʰa→mən i↓↑ fɑŋ↓ts⁼ʊŋ↓ k⁼an↓↑k⁼wan→ t⁼ə xwan→ɥ↑, tʃ⁼i↑tʃ⁼in↓↑ s`əŋ→sə↓ t⁼ə ʃiɑŋ↓↑s`oʊ↓ kʰaɪ→s`ɹ`↓↑,
11.wav|999|i↓↑ xuɑŋ→tʰɑŋ↑, u↑t⁼u↓ əɹ`↑ k⁼ɑʊ↓ts`⁼ʊŋ→.
12.wav|999|ts`⁼ʊŋ↓t⁼wo→ k⁼ɑŋ→k⁼ɑŋ→ tʰa↓ɹ`u↓ s`əŋ→xwo↑ t⁼ə wan↑kʰu↓ts⁼ɹ↓↑t⁼i↓ tʃʰjʊŋ↑s`ə→tʃ⁼i↑ɥ↓,
13.wav|999|ts⁼aɪ↓ liŋ↓ɹ`ən↑ nan↑i↓↑ts`⁼ɹ`↓ʃin↓ t⁼ə tʃ⁼i↑ t⁼wan↓↑s`ɹ`↑tʃ⁼jɛn→ neɪ↓ tʃ⁼joʊ↓ p⁼a↓↑ t⁼a↓p⁼u↓fən↓ tʃ⁼ja→tsʰaɪ↑ xweɪ→xwo↓ t⁼aɪ↓tʃ⁼in↓.
14.wav|999|ts`⁼ə↓ts`⁼ʊŋ↓↑ ts⁼wo↓pʰaɪ↓, tʃʰi↑ k⁼ən→ɥæn↑ tʃʰɥɛ↓s`ɹ`↑ p⁼u↑s`ɹ`↓ p⁼iɛ↑t⁼ə, ts`⁼əŋ↓s`ɹ`↓ u↑liɑʊ↑.
18.wav|999|tʰa→ ɥæn↑ts⁼ɹ↓ s`ɑŋ↓s`u↓ t⁼ə tʃ⁼iŋ→s`ən↑ pʰin↑fa↑ xə↑ kʰʊŋ→ʃɥ→.
16.wav|999|i↑k⁼ə↓ waɪ↓ ts⁼aɪ↓ fu↓joʊ↓↑, t⁼an↓ neɪ↓ts⁼aɪ↓ pʰin↑fa↑ t⁼ə fu↓tʃ⁼ja→ts⁼ɹ↓↑t⁼i↓ laɪ↑t⁼ɑʊ↓ ts`⁼ə↓k⁼ə↓ s`ɹ`↓tʃ⁼iɛ↓,
17.wav|999|xweɪ↓ tʰu↑lɑʊ↑t⁼i↓ jʊŋ↓waɪ↓ ts⁼aɪ↓ t⁼ə tsʰaɪ↑fu↓ tʃʰɥ↓ p⁼u↓↑ts`ʰɑŋ↑ neɪ↓ts⁼aɪ↓ t⁼ə p⁼u↓ts⁼u↑,
18.wav|999|tʰa→ kʰə↓↑uɑŋ↓ tsʰʊŋ↑ waɪ↓p⁼u↓ t⁼ə↑t⁼ɑʊ↓ i→tʃʰiɛ↓,
19.wav|999|ts`⁼ə↓ tʃʰiŋ↑ʃiŋ↑ tʃ⁼joʊ↓ xɑʊ↓↑p⁼i↓↑ s`ɹ`↓tʰu↑ i↓↑ s`ɑʊ↓nɥ↓↑ t⁼ə xan↓s`weɪ↓↑ tʃʰɥ↓ tʃʰiɑŋ↑tʃ⁼jɛn↓ ts⁼ɹ↓tʃ⁼i↓↑ tʰi↓↑pʰwo↓ t⁼ə lɑʊ↓↑ʃjoʊ↓↑ ts`⁼ɹ`→ ɹ`ən↑.
20.wav|999|ɹ`ən↑ ts⁼ɹ↓s`ən→ neɪ↓ts⁼aɪ↓ t⁼ə pʰin↑fa↑ joʊ↑tsʰɹ↓↑ t⁼ɑʊ↓↑ts`⁼ɹ`↓ lə waɪ↓ ts⁼aɪ↓ tsʰaɪ↑fu↓ t⁼ə pʰin↑fa↑.
21.wav|999|ts`⁼ɹ`↓ɥ↑ liŋ↓waɪ↓ liɑŋ↓↑ʃiɑŋ↓ ɹ`ən↑s`əŋ→ xɑʊ↓↑ts`ʰu↓ t⁼ə ts`⁼ʊŋ↓iɑʊ↓ʃiŋ↓,
22.wav|999|p⁼u↓ ʃɥ→iɑʊ↓ wo↓↑ tʰə↓p⁼iɛ↑tʃʰiɑŋ↑t⁼iɑʊ↓.
23.wav|999|tsʰaɪ↑ts`ʰan↓↑ t⁼ə tʃ⁼ja↓ts`⁼ɹ`↑ ts⁼aɪ↓ t⁼ɑŋ→tʃ⁼in→ s`ɹ`↓ ɹ`ən↑ swo↓↑ k⁼ʊŋ→ɹ`ən↓ t⁼ə,
24.wav|999|jʊŋ↓p⁼u↓ts`⁼ə weɪ↓ tʃʰi↑ ʃɥæn→ts`ʰwan↑ tʃ⁼iɛ↓s`ɑʊ↓.
25.wav|999|p⁼i↓↑tʃʰi↓↑ t⁼i↓əɹ`↓ʃiɑŋ↓ t⁼ə xɑʊ↓↑ts`ʰu↓,
26.wav|999|t⁼i↓san→ʃiɑŋ↓ t⁼ə xɑʊ↓↑ts`ʰu↓ tʃ⁼ɥ↓joʊ↓↑ i→ts`⁼ʊŋ↓↑ ʃiɑŋ→t⁼ɑŋ→ pʰiɑʊ→miɑʊ↓↑ t⁼ə ts`ʰəŋ↑fən↓,
27.wav|999|in→weɪ↓ miŋ↑ɥ↓, miŋ↑uɑŋ↓, t⁼i↓weɪ↓ t⁼əŋ↓↑.
28.wav|999|tʃʰɥæn↑ joʊ↑ tʰa→ɹ`ən↑ t⁼ə i↓tʃ⁼jɛn↓ k⁼oʊ↓ts`ʰəŋ↑.
29.wav|999|meɪ↓↑ɹ`ən↑ t⁼oʊ→ kʰə↓↑i↓↑ ts`⁼əŋ→tʃʰɥ↓↑ t⁼ə↑t⁼ɑʊ↓ miŋ↑ɥ↓,
30.wav|999|i↓ tʃ⁼i↑ tʃʰiŋ→p⁼aɪ↑ t⁼ə miŋ↑s`əŋ→,
31.wav|999|t⁼an↓ s`ə↓xweɪ↓ t⁼i↓weɪ↓, ts⁼ə↑ ts`⁼ɹ`↓↑joʊ↓↑ ɥɛ↓ pʰan↓ k⁼wo↑tʃ⁼ja→ ts`⁼əŋ↓fu↓↑ t⁼ə ɹ`ən↑tsʰaɪ↑ nəŋ↑ ɹ`an↓↑ts`⁼ɹ`↓↑,
32.wav|999|ts`⁼ɹ`↓ɥ↑ ʃjɛn↓↑xə↓ t⁼ə miŋ↑uɑŋ↓ tʃ⁼joʊ↓ ts`⁼ɹ`↓↑joʊ↓↑ tʃ⁼i↑s`ɑʊ↓↑s`u↓ ɹ`ən↑tsʰaɪ↑ xweɪ↓ t⁼ə↑t⁼ɑʊ↓.
33.wav|999|ts⁼aɪ↓ swo↓↑joʊ↓↑ ts`⁼ə↓ʃiɛ→ t⁼ɑŋ→ts`⁼ʊŋ→,
34.wav|999|miŋ↑ɥ↓ s`ɹ`↓ mi↑ts⁼u↑ts`⁼ən→k⁼weɪ↓ t⁼ə,
35.wav|999|ʃjɛn↓↑xə↓ t⁼ə miŋ↑uɑŋ↓ ts⁼ə↑ s`ɹ`↓ ɹ`ən↑ swo↓↑ ʃi→uɑŋ↓ t⁼ə↑t⁼ɑʊ↓ t⁼ə tʃ⁼ja↓ts`⁼ɹ`↑ ts`⁼ɹ`↓ɑŋ↑ t⁼ə t⁼ʊŋ→ʃi→,
36.wav|999|na↓ s`ɹ`↓ tʰjɛn→ts`⁼ɹ`→tʃ⁼iɑʊ→ts⁼ɹ tsʰaɪ↑nəŋ↑ t⁼ə↑t⁼ɑʊ↓ t⁼ə tʃ⁼in→ iɑŋ↑mɑʊ↑.
37.wav|999|liŋ↓i↓fɑŋ→mjɛn↓,
38.wav|999|ts`⁼ɹ`↓↑joʊ↓↑ s`a↓↑k⁼wa→ tsʰaɪ↑ xweɪ↓ p⁼a↓↑ s`ə↓xweɪ↓ t⁼i↓weɪ↓ fɑŋ↓ts`⁼ɹ`↓ ts⁼aɪ↓ tsʰaɪ↑ts`ʰan↓↑ ts`⁼ɹ`→tʃʰjɛn↑.
39.wav|999|liŋ↓waɪ↓, ɹ`ən↑ jʊŋ→joʊ↓↑ t⁼ə tsʰaɪ↑ts`ʰan↓↑, u↓pʰin↓↑ xə↑ miŋ↑ɥ↓, s`əŋ→uɑŋ↓,
40.wav|999|s`ɹ`↓ ts`ʰu↓↑ɥ↑ i→ts`⁼ʊŋ↓↑ swo↓↑weɪ↓ t⁼ə xu↓weɪ↓ iŋ↓↑ʃiɑŋ↓↑, tsʰu↓tʃ⁼in↓ t⁼ə k⁼wan→ʃi↓.
41.wav|999|p⁼i↓↑t⁼ə↑ ni↑sɹ→ s`wo→ k⁼wo↓,“ i↑k⁼ə↓ ɹ`ən↑ swo↓↑ jʊŋ→joʊ↓↑ t⁼ə tsʰaɪ↑ts`ʰan↓↑ tʃ⁼ɥɛ↑t⁼iŋ↓ lə ts`⁼ə↓k⁼ə↓ ɹ`ən↑ ts⁼aɪ↓ tʰa→ɹ`ən↑ jɛn↓↑ts`⁼ʊŋ→ t⁼ə tʃ⁼ja↓ts`⁼ɹ`↑”.
42.wav|999|ɹ`u↑k⁼wo↓↑ ts`⁼ə↓tʃ⁼ɥ↓ xwa↓ s`ɹ`↓ ts`⁼əŋ↓tʃʰɥɛ↓ t⁼əxwa↓,
43.wav|999|na↓mə, fan↓↑k⁼wo↓laɪ↑, tʰa→ɹ`ən↑ t⁼weɪ↓ ts⁼ɹ↓tʃ⁼i↓↑ t⁼ə liɑŋ↑xɑʊ↓↑ pʰiŋ↑tʃ⁼ja↓,
44.wav|999|nəŋ↑i↓↑ k⁼ə↓ts`⁼ʊŋ↓↑ ʃiŋ↑s`ɹ`↓ p⁼ɑŋ→ts`⁼u↓ ts⁼ɹ↓tʃ⁼i↓↑ xwo↓tʃʰɥ↓↑ tsʰaɪ↑ts`ʰan↓↑.
45.wav|999|nəŋ↑i↓↑ k⁼ə↓ts`⁼ʊŋ↓↑ ʃiŋ↑s`ɹ`↓ p⁼ɑŋ→ts`⁼u↓ ts⁼ɹ↓tʃ⁼i↓↑ xwo↓tʃʰɥ↓↑ tsʰaɪ↑ts`ʰan↓↑.
46.wav|999|soʊ fɑɹ, ðə ˈpɹɑbləmz hæv əˈkəɹd ɪn ðə ˈjuˈɛs.
47.wav|999|ðə ˈsteɪtmənt kənˈteɪnd noʊ səˈpɹaɪzɪz.
48.wav|999|ðə pəɹˈfɔɹməns kənˈtɪnjuz tɪ ˌɪmˈpɹuv.
49.wav|999|ðət wʊd bi ə ˈdeɪndʒəɹ.
50.wav|999|ˈfɹæŋkli, wi wəɹ ˈləki tɪ gɪt ˈsɛkənd.
51.wav|999|ˌhaʊˈɛvəɹ, noʊ ˈfəɹðəɹ ˈækʃən wɑz ˈteɪkən baɪ pəˈlis.
52.wav|999|ɪt dɪˈpɛndz ɔn ˈleɪbəɹ, nɑt ɔn ˈjuˈɛs.
53.wav|999|aɪ hæd tɪ bi ˈɪntu ɪt.
54.wav|999|maɪ vju həz ˈɔlˌweɪz bɪn ðə seɪm.
55.wav|999|ɪn fækt, hi ɪz nɑt ˈivɪn ɪn ðə skwɑd fəɹ ðə geɪm.
56.wav|999|ɪt ɪz sɛt ɪn ˈpɛɹɪs.
57.wav|999|du ju θɪŋk wɪɹ ə tɔp ˈneɪʃən.
58.wav|999|ɪz ɑɹ ˈtʃɪldɹən ˈləɹnɪŋ.
59.wav|999|haʊ ˌɪndɪˈpɛndənt ɪz ðət.
60.wav|999|wət fɔɹm dɪd ðət teɪk.
61.wav|999|əɹ ðeɪ fɹi.
62.wav|999|ðə ˈpɑɹti həz ˈnɛvəɹ ˈfʊli ɹɪˈkəvəɹd.
63.wav|999|ɪn ˈmɛni weɪz, ðət ɪz ɛz ˌɪmˈpɔɹtənt.
64.wav|999|hɪz ˈlidəɹ ˈwɔntɪd tɪ ˈsɛləˌbɹeɪt.
65.wav|999|ɪt ɪz nɑt ən ˈɔpʃən, bət ə ˈpɑləsi ɹɪkˈwaɪɹmənt.
66.wav|999|əv ˈfəɹðəɹ ˈpɹaɪvəsi, hi hæd noʊ nid.
67.wav|999|hi wɑz gʊd, bət nɑt ðət gʊd.
68.wav|999|ðə ɹɪˈzəɫts əɹ ɪkˈspɛktɪd wɪˈθɪn deɪz.
69.wav|999|ðə ˈkəmpəˌni stɪɫ hæd ðə ˈkɑnfədɛns əv ɪts ˈbæŋkəɹz, hi sɛd.
70.wav|999|ˈmɪstəɹ ˈkɹɔfəɹd ɪz noʊ ˈstɹeɪndʒəɹ tɪ ˈskɑtɪʃ ˈɛnəɹˌpɹaɪz.
71.wav|999|wi wəɹ wɛɫ wəɹθ ðə wɪn.
72.wav|999|ənd ɪt wɑz ðɪs wən.
73.wav|999|bət ðæts əˈnəðəɹ ˈstɔɹi fəɹ əˈnəðəɹ deɪ.
74.wav|999|bət ɪt wɑz ˈɔlsoʊ hɑɹd.
75.wav|999|ɪf soʊ, ɪt ɪz ə ˈfəni taɪm tɪ ˌɪntɹəˈdus ɪt.
76.wav|999|wɪɹ ˈlʊkɪŋ fəɹ ˈjunɪti ɪn ðə ˈkaʊnsəɫ.
77.wav|999|baɪ ðɛn, ə ˈmæsɪv ˈligəɫ ˈbætəɫ ɪz ˈlaɪkli tɪ hæv ˈstɑɹtɪd.
78.wav|999|ðeɪ əɹ nɑt goʊɪŋ ðə lɛŋθ əv ðə pɪtʃ.
79.wav|999|ˌhaʊˈɛvəɹ, hi ˈlæstɪd ˈoʊnli faɪv deɪz ˌbiˈfɔɹ goʊɪŋ ɔn ðə ɹən.
80.wav|999|ðə kənˈsəɹnz əɹ ðə seɪm.
81.wav|999|ðət wɑz ə ˈboʊnəs, bət ɪt wɑz nɑt ðə meɪn əˈbdʒɛktɪv.
82.wav|999|ˈskɑtlənd wən baɪ sɪks ˈwɪkəts.
83.wav|999|ɪt həz tɪ bi ɛnˈfɔɹst.
84.wav|999|ənd wi kən du ɪt.
85.wav|999|ˈɛvəɹi wən ɪz ə ˈwɪnəɹ.
86.wav|999|dɹəgz əɹ juzd ə lɔt æt ðə ˈfɪʃɪŋ, nɑt dʒɪst ˈkænəbəs.
87.wav|999|ðeɪ hæv sɛt ðə ˈstændəɹd.
88.wav|999|ðɪs ɪz ə ˈmeɪdʒəɹ ˈpɹɑpəɹti ɪn ˈɛdənbəɹoʊ.
89.wav|999|ɪt ɪz ə ˈkɹaɪsəs əv ˈjumən ɹaɪts, ə ˈkɹaɪsəs əv ˈlidəɹˌʃɪp.
90.wav|999|ðə ɪgˈzɪstɪŋ ˈstɹəktʃəɹ ɪz ɪn ˈtɹəbəɫ.
91.wav|999|ɪt ɔɫ ˈstɑɹtɪd wɪθ ə ˈvɪzɪtɪŋ ˈɹəgbi kləb.
92.wav|999|hi ɹɪfˈjuzd tɪ ɹɪˈviɫ ðə ˈɹizənz fəɹ ðə splɪt.
user_voice_collect.py
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import numpy as np
import torch
import torchaudio
import gradio as gr
import os
anno_lines = []
with open("./user_voice/user_voice.txt", 'r', encoding='utf-8') as f:
for line in f.readlines():
anno_lines.append(line.strip("\n"))
text_index = 0
def display_text(index):
index = int(index)
global text_index
text_index = index
return f"{text_index}: " + anno_lines[index].split("|")[2].strip("[ZH]").strip("[EN]")
def display_prev_text():
global text_index
if text_index != 0:
text_index -= 1
return f"{text_index}: " + anno_lines[text_index].split("|")[2].strip("[ZH]").strip("[EN]")
def display_next_text():
global text_index
if text_index != len(anno_lines)-1:
text_index += 1
return f"{text_index}: " + anno_lines[text_index].split("|")[2].strip("[ZH]").strip("[EN]")
def save_audio(audio):
global text_index
if audio:
sr, wav = audio
wav = torch.tensor(wav).type(torch.float32) / max(wav.max(), -wav.min())
wav = wav.unsqueeze(0) if len(wav.shape) == 1 else wav
if sr != 22050:
res_wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=22050)(wav)
else:
res_wav = wav
torchaudio.save(f"./user_voice/{str(text_index)}.wav", res_wav, 22050, channels_first=True)
return f"Audio saved to ./user_voice/{str(text_index)}.wav successfully!"
else:
return "Error: Please record your audio!"
if __name__ == "__main__":
app = gr.Blocks()
with app:
with gr.Row():
text = gr.Textbox(value="0: " + anno_lines[0].split("|")[2].strip("[ZH]"), label="Please read the text here")
with gr.Row():
audio_to_collect = gr.Audio(source="microphone")
with gr.Row():
with gr.Column():
prev_btn = gr.Button(value="Previous")
with gr.Column():
next_btn = gr.Button(value="Next")
with gr.Row():
index_dropdown = gr.Dropdown(choices=[str(i) for i in range(len(anno_lines))], value="0",
label="No. of text", interactive=True)
with gr.Row():
with gr.Column():
save_btn = gr.Button(value="Save Audio")
with gr.Column():
audio_save_message = gr.Textbox(label="Message")
index_dropdown.change(display_text, inputs=index_dropdown, outputs=text)
prev_btn.click(display_prev_text, inputs=None, outputs=text)
next_btn.click(display_next_text, inputs=None, outputs=text)
save_btn.click(save_audio, inputs=audio_to_collect, outputs=audio_save_message)
app.launch()
whisper_transcribe.py
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@@ -1,90 +0,0 @@
import whisper
import os
import torchaudio
import json
lang2token = {
'zh': "[ZH]",
'ja': "[JA]",
"en": "[EN]",
}
def transcribe_one(audio_path):
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(audio_path)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
print(f"Detected language: {max(probs, key=probs.get)}")
lang = max(probs, key=probs.get)
# decode the audio
options = whisper.DecodingOptions()
result = whisper.decode(model, mel, options)
# print the recognized text
print(result.text)
return lang, result.text
if __name__ == "__main__":
model = whisper.load_model("medium")
parent_dir = "./custom_character_voice/"
speaker_names = list(os.walk(parent_dir))[0][1]
speaker2id = {}
speaker_annos = []
# resample audios
for speaker in speaker_names:
speaker2id[speaker] = 1000 + len(speaker2id)
for i, wavfile in enumerate(list(os.walk(parent_dir + speaker))[0][2]):
# try to load file as audio
if wavfile.startswith("processed_"):
continue
try:
wav, sr = torchaudio.load(parent_dir + speaker + "/" + wavfile, frame_offset=0, num_frames=-1, normalize=True,
channels_first=True)
wav = wav.mean(dim=0).unsqueeze(0)
if sr != 22050:
wav = torchaudio.transforms.Resample(orig_freq=sr, new_freq=22050)(wav)
if wav.shape[1] / sr > 20:
print(f"{wavfile} too long, ignoring\n")
save_path = parent_dir + speaker + "/" + f"processed_{i}.wav"
torchaudio.save(save_path, wav, 22050, channels_first=True)
# transcribe text
lang, text = transcribe_one(save_path)
if lang not in ['zh', 'en', 'ja']:
print(f"{lang} not supported, ignoring\n")
text = lang2token[lang] + text + lang2token[lang] + "\n"
speaker_annos.append(save_path + "|" + str(speaker2id[speaker]) + "|" + text)
except:
continue
# clean annotation
import text
cleaned_speaker_annos = []
for i, line in enumerate(speaker_annos):
path, sid, txt = line.split("|")
if len(txt) > 100:
continue
cleaned_text = text._clean_text(txt, ["cjke_cleaners2"])
cleaned_text += "\n" if not cleaned_text.endswith("\n") else ""
cleaned_speaker_annos.append(path + "|" + sid + "|" + cleaned_text)
with open("custom_character_anno.txt", 'w', encoding='utf-8') as f:
for line in cleaned_speaker_annos:
f.write(line)
# generate new config
with open("./configs/finetune_speaker.json", 'r', encoding='utf-8') as f:
hps = json.load(f)
# modify n_speakers
hps['data']["n_speakers"] = 1000 + len(speaker2id)
# add speaker names
for speaker in speaker_names:
hps['speakers'][speaker] = speaker2id[speaker]
# save modified config
with open("./configs/modified_finetune_speaker.json", 'w', encoding='utf-8') as f:
json.dump(hps, f, indent=2)
print("finished")